Systems and methods for identifying deployed cables

ABSTRACT

In some implementations, a system may receive a cable map for a deployed cable. The system may receive vibration data indicating a vibration associated with a first section of the cable. The system may determine a characteristic associated with the first section of the cable based on the vibration. The system may determine a location associated with the characteristic based on the cable map. The system may determine that the first section of the cable is associated with the location based on the location being associated with the characteristic. The system may associate the location and a length of a second section of the cable extending from an initial location to the location. The system may receive an input identifying the length of the second section of the cable and may output the location based on associating the location and the length of the second section of the cable.

BACKGROUND

To maintain an integrity of deployed fiber cables, network serviceproviders need to repair fiber cable problems in the field, such as afiber cut, high loss splice points, tightly bending points, and/or thelike. When a deployed fiber cable experiences a fault (e.g., a fibercut), a field technician may be deployed to correct the issue. The fieldtechnician needs to identify a location of the fault quickly so that thefield technician may travel to the location and correct the fault in thefiber cable.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1M are diagrams of an example associated with identifyinglocations of deployed cables.

FIG. 2 is a diagram illustrating an example of training and using amachine learning model in connection with identifying locations ofdeployed cables.

FIG. 3 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 4 is a diagram of example components of one or more devices of FIG.3 .

FIG. 5 is a flowchart of an example process relating to identifyinglocations of deployed cables.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

Current techniques do not accurately identify locations of a deployedfiber cable. Thus, when a fault occurs in the fiber cable, a fieldtechnician may not quickly identify a location of the fault and may notquickly travel to the location and correct the fault in the fiber cable.Current techniques fail to identify locations of deployed fiber cablefor several reasons. For example, to identify a location of a fault in afiber cable, a technician may utilize an optical time- domainreflectometer (OTDR) to measure cable distance from a central office tothe location of the fault. However, the cable distance measured by theOTDR fails to provide a geographical distance between the central officeand the location of the fault. Moreover, the distance measured by theOTDR cannot be accurately correlated to a geographic location, becauseeach deployed fiber cable may include uncertain amounts of localizedpath deviations or spare cable looping (e.g., a slack loop) alongdeployed routes. A difference between a geographic location derived fromthe OTDR-measured cable distance based on a route layout and the actualgeographical location may be very large. Therefore, a lot of guessworkmay be involved in identifying a particular location of a fault in afiber cable, which results in wasted time in finding the right locationto inspect/repair, as well as potential additional cost indestruction/reconstruction of areas around the fiber cable simply toidentify the correct location of the fault. Thus, current techniques foridentifying locations of deployed fiber cable waste computing resources(e.g., processing resources, memory resources, and/or communicationresources, among other examples), networking resources, transportationresources, human resources, and/or the like associated with identifyingan incorrect location of a fault in a fiber cable, traveling to theincorrect location, accessing the fiber cable underground at theincorrect location, re-identifying a correct location of the fault inthe fiber cable, recovering lost network traffic, and/or the like.

U.S. patent application Ser. No. 16/813,269, titled “Systems and Methodsfor Determining Fiber Cable Geographic Locations,” filed Mar. 9, 2020,the disclosure of which is considered part of and is incorporated byreference into this Patent Application, describes a measurement platformthat may be used to identify locations of a deployed fiber cable basedon vibrations generated by a vibration device along a deployed route ofa fiber cable. However, in some cases, the time and/or manpower requiredto deploy the vibration device along the deployed route of the fibercable may not be available and/or may not be performed prior to anoccurrence of a fault.

Some implementations described herein provide a system for identifyinglocations of a deployed fiber cable based on vibrations caused byenvironmental characteristics (e.g., vibrations caused by vehicletraffic and/or weather, among other examples). For example, the systemmay receive, from a sensor device, vibration data. The vibration datamay include cable distance data identifying one or more cable distancesalong the fiber cable to one or more vibrations experienced by the fibercable and vibration data associated with the one or more vibrationsexperienced by the fiber cable.

In the event of an alarm condition (e.g., a fault) associated with thefiber cable, a sensor device may determine a cable distance to the alarmcondition, and the system may receive data identifying the cabledistance to the alarm condition. The system may determine a location ofthe alarm condition based on the correlated location data and using thedata identifying the cable distance along the fiber cable to the alarmcondition. The system may output the location of the alarm condition toa user device associated with a user (e.g., a technician).

In this way, the system may quickly identify a location of a fault in afiber cable so that an action may be taken to avoid the fault and/orservice the fault in the fiber cable. Thus, the system conservescomputing resources (e.g., processing resources, memory resources,and/or communication resources, among other examples), networkingresources, transportation resources, human resources, and/or the likethat would otherwise have been utilized in identifying an incorrectlocation of a fault in a fiber cable, traveling to the incorrectlocation, re-identifying a correct location of the fault in the fibercable, recovering lost network traffic, and/or the like.

FIGS. 1A-1M are diagrams of an example 100 associated with identifyinglocations of deployed cables. As shown in FIGS. 1A-1M, example 100includes a system 110 and a sensor device 115. The system 110 mayinclude one or more devices configured to identify locations of adeployed fiber cable based on vibrations experienced by the deployedcable, as described herein. The sensor device 115 may include an opticaldevice, such as a distributed optical fiber sensing device (e.g., aRayleigh scattering based distributed optical fiber acoustic sensingdevice), an optical reflectometry device (e.g., an optical time-domainreflectometry (OTDR) device), and/or a computer, among other examples,that may determine a length of the deployed cable from an initiallocation (e.g., a location of the sensor device 115 and/or the centraloffice of a service provider associated with the deployed cable) to avibration experienced by the deployed cable and may sense vibrations inthe deployed cable caused by an environmental characteristic associatedwith the deployed cable.

As shown in FIG. 1A, and by reference number 120, the system 110 mayreceive cable map data associated with the deployed cable. The cable mapdata may correspond to a cable map indicating a route of the deployedcable within a geographical area (e.g., within a city, a neighborhood,and/or a region, among other examples). For example, the cable map datamay include location information (e.g., geographical coordinates, suchas a latitude and a longitude) indicating a geographic location of thedeployed cable within the geographical area.

In some implementations, the cable map data may include additionalinformation associated with the geographical area and/or the deployedcable. For example, the cable map data may include informationidentifying a set of points along the route of the deployed cable. Theset of points may correspond to junction points connecting one sectionof cable with another section of cable, a location of an access panel,and/or a location of an access point for accessing the cable (e.g., amanhole), among other examples.

In some implementations, the additional information may includeinformation indicating an environmental condition associated with asection of the geographical area. For example, the additionalinformation may indicate a location of a traffic signal, a volume oftraffic associated with a roadway, a location of a particular type ofstructure (e.g., a bridge, a school, a park, among other examples),and/or a location of a construction site, among other examples.

In some implementations, the cable map data may be input by a user via auser interface associated with the system 110. Alternatively, and/oradditionally, the system 110 may receive (e.g., based on sending arequest) the cable map data from another device (e.g., a server deviceof a network provider associated with the deployed cable and/or thesensor device 115, among other examples).

As shown in FIG. 1B, and by reference number 125, the system 110 maydetermine a series of points along a route of the deployed cable basedon the cable map data. In some implementations, the series of points maycorrespond to the set of points indicated by the cable map data. Thesystem 110 may determine the series of points along the route of thedeployed cable based on extracting information associated with the setof points from the cable map data.

As shown by reference number 130, the system 110 may determineenvironmental characteristics associated with the series of points. Insome implementations, the system 110 determines the environmentalcharacteristics based on the cable map data. For example, the cable mapdata may include information identifying the environmental charactersfor a series of points along the route of the deployed cable.Alternatively, and/or additionally, the system 110 may determine theenvironmental characteristics based on user input. For example, a usermay input (e.g., via a user interface associated with the system 110)information identifying environmental characteristics associated withone or more points along the route of the deployed cable.

As shown in FIG. 1C, and by reference number 135, the sensor device 115may sense vibrations experienced by the deployed cable and may generatevibration data based on sensing the vibrations. The sensor device 115may detect a vibration generated by an environmental characteristic(e.g., a vehicle traveling along a roadway adjacent to the route of thedeployed cable) and may generate vibration data indicating a vibrationcharacteristic of the vibration experienced by the deployed cable(described in greater detail below).

In some implementations, the vibration data may include cable distancedata indicating a length of cable extending from the sensor device 115to the section of the deployed cable experiencing the vibration. Thesensor device 115 may determine a distance along the deployed cable tothe vibration. For example, the sensor device 115 may provide a firstoptical signal (e.g., light) to the deployed cable while a firstvibration event is occurring, and the first optical signal may be (atleast partially) reflected back to the sensor device 115 from a firstlocation along the route of the deployed cable where the first vibrationis experienced. The sensor device 115 may detect (using the distributedoptical sensing device) the first optical signal reflected back from thefirst location to the sensor device 115 and may determine a first cabledistance from the first location to a location of the sensor device 115(e.g., based on the speed of light through the deployed cable and basedon the first optical signal reflected back from the first location tothe sensor device 115).

The sensor device 115 may provide a second optical signal to thedeployed cable while a second vibration is being experienced by thedeployed cable, and the second optical signal may be reflected back tothe sensor device 115 from a second location along the route of thedeployed cable where the second vibration is experienced. The sensordevice 115 may detect the second optical signal reflected back from thesecond location to the sensor device 115, and may determine a secondcable distance from the second location to the location of the sensordevice 115. This process may repeat until cable distances are determinedby the sensor device 115 for each point along the route of the deployedcable where vibrations are experienced.

The vibration and measurement process may be performed whilecommunications traffic is being carried by the deployed cable, as thevibrations experienced by the fiber cable do not prevent thetransmission of optical signals between the endpoints of the deployedcable. Moreover, no modifications need to be made to the surroundings ofthe deployed cable (e.g., excavation, unmounting, rehanging) or thecable itself (e.g., cutting, splicing) in order to obtain the reflectedoptical signals used for the cable distance measurement, as thevibrations experienced by the cable create the conditions for opticalreflection (e.g., back scattering) that may be used by the distributedoptical fiber sensing device to obtain the vibration data and/or tomeasure cable distance. In some implementations, the optical signalsused to perform cable measurements may use wavelengths that are outsidethose used for carrying communications traffic. Thus, the cable locationprocesses and systems described herein effectively obtain cable locationdata without impacting the operation of the cable or the environmentaround the cable.

As shown in FIG. 1D, and by reference number 140, the system 110 mayreceive, for example, from the sensor device 115, vibration data. Thevibration data may indicate a vibration characteristic of a vibrationexperienced by a section of the deployed cable and/or cable distancedata identifying a corresponding length of a section of the deployedcable from an initial location (e.g., the location of the sensor device115) to the vibration. In some implementations, the cable distance datamay also include measurement identifiers (IDs) that identify, forexample, a number of each of the measurements made on the deployedcable, and the data identifying corresponding cable distances from thelocation of the sensor device 115 to the vibration location may beassociated with the measurement number. For example, the cable distancedata may be represented as a table with a measurement ID field thatincludes entries for measurement IDs (measurement 1, 2, 3, . . . , N)and a vibration distance field that includes entries for correspondingcable distances along the deployed cable from the sensor device 115 toeach detected vibration (e.g., 1,000 meters, 4,500 meters, 15,000meters, and/or the like).

In some implementations, the system 110 may store vibration data in adata structure (e.g., a database, a table, a list, and/or the like)associated with the system 110. In some implementations, the vibrationdata is collected once by the sensor device 115, provided to the system110, and stored in the data structure. Alternatively, if the route ofthe deployed cable is updated, the sensor device 115 may repeat theprocess described above in order to collect updated cable distance datafor the deployed cable. The sensor device 115 may provide the updatedcable distance data to the system 110, and the system 110 may update thevibration data stored in the data structure.

As shown by reference number 145, the system 110 may determine vibrationcharacteristics based on the vibration data. In some implementations,the system 110 may determine the vibration characteristics based onproviding the vibration data as an input to a machine learning modelconfigured to generate an output indicating a vibration characteristicbased on the vibration data.

In some implementations, the system 110 may train the machine learningmodel to determine the vibration characteristic. The machine learningmodel may be trained based on historical data relating to vibrationsexperienced by deployed cable and historical data relating to vibrationcharacteristics with which the vibrations are associated. The machinelearning model may be trained to determine, based on vibration dataassociated with a vibration experienced by a deployed cable, a vibrationcharacteristic with which the vibration is associated and a confidencescore that reflects a measure of confidence that the vibrationcharacteristic is accurate for this vibration. In some implementations,the system 110 trains the machine learning model in a manner similar tothat described below with respect to FIG. 2 . Alternatively, and/oradditionally, the system 110 may obtain a trained machine learning modelfrom another device (e.g., a server device of a service providerassociated with the deployed cable).

In some implementations, the system 110 may determine, based on thevibration data, that a vibration is associated with vibrationcharacteristic associated with a direction of traffic along a roadway.For example, as shown in FIG. 1E, the system 110 may determine that avibration is associated with vehicle traffic traveling in an east-westdirection based on an increase (e.g., when the sensor device ispositioned to the east of the location of the vibration) in cabledistances associated with a series of vibrations experienced by thedeployed cable over a time period. The system 110 may determine that avibration is associated with vehicle traffic traveling in a west-eastdirection based on a decrease (e.g., when the sensor device ispositioned to the east of the location of the vibration) in cabledistances associated with a series of vibrations experienced by thedeployed cable over a time period.

As further shown in FIG. 1E, the system 110 may determine a rate ofspeed of traffic traveling over a roadway along a section of deployedcable based on a length of time that a vibration is experienced by asection of the deployed cable. For example, the system 110 may determinea rate of speed of traffic based on an amount of time between a time atwhich a vibration is experienced at a first point along the deployedcable and a time at which a corresponding vibration is experienced at asecond point along the deployed cable and a geographical distancebetween the first point and the second point.

In some implementations, the vibration characteristic may be associatedwith a volume of vehicular traffic. For example, as shown in FIG. 1F,the system 110 may determine a volume of traffic associated with asection of the deployed cable based on a frequency at which a series ofvibrations are experienced by the section of the deployed cable.

In some implementations, the vibration characteristic may be associatedwith the deployed cable extending across and/or under a roadway. Forexample, as shown in FIG. 1G, the system 110 may determine that avibration is associated with a vibration characteristic indicating thatthe deployed cable extends across and underneath a roadway based on thevibration data indicating a series of pulses of vibrations at aparticular point along the section of the deployed cable.

In some implementations, the vibration characteristic may be associatedwith a traffic signal. For example, as shown in FIG. 1H, the system 110may determine that a vibration is associated with a vibrationcharacteristic indicating a presence of a traffic signal based on thevibration data indicating that the vibration is experienced by adjacentportions of the section of the deployed cable, followed by a particularportion of the section of the deployed cable experiencing the vibrationfor a time period, followed by the vibration being experienced by aseries of portions adjacent to the particular portion of the section ofthe deployed cable.

In some implementations, the vibration characteristic may be associatedwith a presence of a slack loop. For example, as shown in FIG. 1I, thesystem 110 may determine that a vibration is associated with a vibrationcharacteristic indicating a presence of a slack loop based on thevibration data indicating that the vibration is experienced by adjacentportions of the section of the deployed cable, followed by a set ofadjacent portions of the section of the deployed cable experiencing thevibration at a same time, followed by the vibration being experienced bya series of portions adjacent to the set of adjacent portions of thesection of the deployed cable.

As shown in FIG. 1J, and by reference number 150, the system 110 maydetermine a point, of the set of points, associated with a section ofdeployed cable based on the vibration characteristics and theenvironmental characteristics. As an example, the vibrationcharacteristic may indicate a presence of a traffic signal. The system110 may determine a point along the route of the deployed cable at whicha traffic signal is present. For example, the system 110 may determinethe point based on additional information that is included in the cablemap data and that indicates that a traffic signal is located at aparticular point along the route of the deployed cable.

In some implementations, the system 110 determines a point along theroute of the deployed cable based on a difference between a vibrationpattern of a vibration experienced by a first section of the deployedcable and a vibration pattern of a vibration experienced by a secondsection of the deployed cable that is adjacent to the first section. Asan example, a first section of the deployed cable may be deployedunderground (e.g., may be a buried cable). At a particular point, thefirst section of the deployed cable may connect to a second portion ofthe deployed cable. The second portion of the deployed cable may bedeployed above ground (e.g., may be a suspended cable).

The system 110 may determine that a vibration characteristic associatedwith a vibration experienced by the first section of the deployed cableis associated with a buried cable adjacent to a roadway experiencing avolume of traffic at a particular rate of speed. The system 110 maydetermine that a vibration characteristic associated with a vibrationexperienced by the second section of the deployed cable is associatedwith a suspended cable experiencing vibrations caused by wind and/oranother environmental condition.

In some implementations, the system 110 may determine that the firstsection of the deployed cable is adjacent to the second section of thedeployed cable based on the cable distances associated with thevibrations experienced by the first and second sections of the deployedcable. The system 110 may identify a location at which the deployedcable transitions from being deployed underground to being deployedabove ground based on the cable map data. The system 110 may determinethat the identified location corresponds to a point on the deployedcable at which an end of the first section of the deployed cable isadjacent to an end of the second section of the deployed cable.

As shown in FIG. 1K, and by reference number 155, the system 110 maydetermine a geographical distance to the point, a length of deployedcable from an initial location to the point, and a difference betweenthe geographical distance to the point and the length of deployed cable.The system 110 may determine the geographical distance to the pointbased on the cable map data. For example, the cable map data may includeadditional information indicating the location of the point associatedwith an environmental characteristic corresponding to the vibrationcharacteristic.

The system 110 may determine a length of the deployed cable from aninitial location (e.g., a location of the sensor device 115) to thepoint based on the vibration data. For example, the vibration data mayinclude cable distance data indicating a length of the deployed cablefrom the initial location to the location of the section of the deployedcable experiencing the vibration from which the vibration characteristicwas determined. The system 110 may determine the difference between thegeographical distance and the length of the deployed cable based onsubtracting the geographical distance from the length of the deployedcable.

The difference may indicate an amount of additional cable used to deploythe cable from the initial location to the location at which thevibration was experienced. For example, the different may correspond toa length of the deployed cable included in a slack loop at an accesspanel located along the section of the deployed cable. In someimplementations, the system 110 may determine a length of the deployedcable included in a plurality of slack loops. For example, the system110 may identify a plurality of points, of the set of points indicatedby the cable map data, that are associated with a respective slack loop.In some implementations, the cable map data may indicate that theplurality of points are associated with the slack loops. In someimplementations, the system 110 may determine that the plurality ofpoints are associated with the slack loops based on the plurality ofpoints being associated with a junction and/or an access panel, amongother examples. The system 110 may determine a length of the deployedcable included in each slack loop, of the plurality of slack loops, in amanner similar to that described above.

As shown by reference number 160, the system 110 may generate deployedcable data based on the difference between the geographical distance andthe length of the deployed cable. The system 110 may generate thedeployed cable data based on correlating the cable distance data, thedifference between the geographical distance and the length of thedeployed cable, and/or the location data associated with the point. Forexample, as shown in FIG. 1L, the deployed cable data may be representedas a table. The table may include, for each point experiencing avibration and/or indicated in the cable map data, a location IDassociated with the point, a length of the deployed cable from theinitial location to the location of the point (e.g., an OTDR distancefrom the central office to the point, as shown in FIG. 1L), ageographical distance from the initial location to the location of thepoint (e.g., a geographical distance from the central office to thepoint, as shown in FIG. 1L), information for enabling a user to identifythe point (e.g., a name of a building located at the location of thepoint, a landmark associated with the point, and/or informationidentifying an intersection of two roadways at the location of thepoint, among other examples), and/or location data indicating thelocation of the point.

The system 110 may store cable map data in the data structure associatedwith system 110. In some implementations, the cable map data isdetermined once by the system 110 and stored in the data structure.Alternatively, if the route of the deployed cable is updated ormodified, the sensor device 115 may repeat the process described abovein order to collect updated vibration data for the deployed cable. Thesensor device 115 may provide the updated vibration data to the system110. The system 110 may obtain updated cable map data and may generateupdated deployed cable data in a manner similar to that described above.

In some implementations, as shown in FIG. 1M, the deployed cable datamay include information associated with slack loops present along thedeployed cable. As shown, the information associated with a slack loopmay include a location ID associated with the slack loop, a length ofdeployed cable from an initial location (e.g., a location of the sensordevice 115) to a location of the slack loop (e.g., an OTDR distance fromthe central office, as shown in FIG. 1M), a geographical distance fromthe initial location to the location of the slack loop, a length ofcoiled cable included in the slack loop (e.g., the difference betweenthe geographical distance and the length of the deployed cable), andlocation data indicating a geographical location of the slack loop.

In some implementations, the system 110 may update a cable mapassociated with the cable map data to include the deployed cable data.For example, the system 110 may update the cable map to show thegeographical distance to each point, of the series of points, identifiedalong the route of the deployed cable, the OTDR distance from theinitial point to each point, the location of a slack loop, and/or alength of coiled cable included in a slack loop, among other examples.

In some implementations, an alarm condition may occur in the deployedcable. The alarm condition may be a result of a cut in the deployedcable, a high loss splice point, and/or a tightly bending point, amongother examples, associated with the deployed cable. For purposes of thisexample, if different cable distances along the deployed cable arelabeled “A” through “Z” (e.g., as shown in FIG. 1B), the alarm conditionmay occur at a cable distance (e.g., from the sensor device 115 and/orthe central office) nearby a location labeled “C.” The alarm conditionmay be detected by a network device (e.g., a network provider server)that manages a network associated with the deployed cable.

In some implementations, as a result of the alarm condition, the sensordevice 115 may be activated to determine a cable distance along thedeployed cable to the cause of the alarm condition. For example, thesensor device 115 may provide an optical signal to the deployed cable,and the cause of the alarm condition (e.g., a displacement or a fibercut) associated with the deployed cable may cause the optical signal tobe reflected back to the sensor device 115 from a location of the alarmcondition. The sensor device 115 may detect the optical signal reflectedback from the location of the alarm condition to the sensor device 115,and may determine a cable distance from the location of the alarmcondition to a location of the sensor device 115. In this example, themeasurement may be determined to be 800 feet (e.g., as shown in FIG.1L).

The system 110 may receive, from the sensor device 115, data identifyingthe cable distance along the fiber cable to the alarm condition. Forexample, the system 110 may receive data identifying the detected cabledistance from the location of the sensor device 115 (e.g., the locationof the central office) to the cause of the alarm condition associatedwith the deployed cable. In this example, the measurement of 800 feetmay be provided to the measurement platform.

In some implementations, the system 110 may determine a location of thealarm condition based on the deployed cable data. Continuing with thecurrent example, where the cable distance along the fiber cable to thealarm condition is determined to be 800 feet, the system 110 may use thedeployed cable data to determine a geographical location closest to thealarm condition—in this case, “Road B x Road C” (e.g., the intersectionof road B and road C). In some implementations, the system 110 may alsoprovide a geographical distance from the location (CO) of the sensordevice 115 to the geographical location closest to the alarm condition(e.g., 650 feet, as shown in FIG. 1L).

In some implementations, based on the determined geographic location ofthe cause of the alarm condition, the system 110 may perform one or moreactions. In some implementations, the one or more actions include thesystem 110 determining directions (e.g., a navigation route) to thecause of the alarm condition based on the location of the alarmcondition. For example, if system 110 determines that the alarmcondition is located at the intersection of road B and road C, thesystem 110 may calculate directions to the alarm condition, for example,from the central office, a nearby service center, or another startingpoint. In this way, system 110 may quickly provide directions to atechnician and/or a vehicle for repairing the deployed cable, whichconserves resources that would otherwise have been utilized in manuallydetermining directions to the technician and/or the vehicle, causing thetechnician and/or the vehicle to travel based on potentially incorrectdirections, and/or the like.

In some implementations, the one or more actions include the system 110causing a vehicle to be dispatched for servicing the deployed cable. Forexample, the system 110 may cause an autonomous automobile (e.g., a car,a truck, a van, and/or the like) to be dispatched for servicing thedeployed cable. In this way, system 110 may utilize existing land routesand infrastructure to service the deployed cable, thereby conservingresources (e.g., computing resources, networking resources, and/or thelike) that would otherwise have been utilized in scheduling a repairservice, assigning a technician for the repair service, and/or the like.

In some implementations, the one or more actions include the system 110causing an airborne autonomous vehicle to be dispatched for servicingthe deployed cable. For example, system 110 may cause an unmanned aerialvehicle to be dispatched for servicing the deployed cable. In this way,the system 110 may service the deployed cable at locations that may berestricted by automobile traffic, traffic controls, inaccessibleroadways, and/or unnavigable terrain, among other examples, therebyconserving resources that would otherwise would have been utilizedservicing the deployed cable at the locations in a more expensive ormore time-consuming manner (e.g., with larger and/or heavier vehicles,slower vehicles, and/or less direct routes, among other examples).

In some implementations, the one or more actions include the system 110causing a technician to be dispatched for servicing the deployed cable.For example, the system 110 may automatically identify an availabletechnician nearest to the alarm condition and may instruct thetechnician to travel to the alarm condition and service the deployedcable. In this way, the system 110 may enable servicing of the deployedcable, thereby conserving resources (e.g., computing resources,networking resources, and/or the like) that would otherwise have beenutilized in scheduling a repair service, assigning a technician for therepair service, and/or the like.

In some implementations, the one or more actions include the system 110causing an order for new fiber cable to be placed for repairing thedeployed cable. For example, the system 110 may automatically invoke aprovider of fiber cable to deliver the new fiber cable to the locationof the alarm condition. In some implementations, the system 110 maycause the new fiber cable to be autonomously delivered to the locationof the alarm condition. In this way, the system 110 may automaticallycause the new fiber cable to be provided at the location that requiresthe new fiber cable for repairing the deployed cable.

In some implementations, the one or more actions include the system 110redirecting network traffic from the deployed cable to another fibercable. For example, the system 110 may identify network trafficassociated with the deployed cable, and may identify another fiber cablethat is available and can handle the network traffic. The system 110 maythen redirect the network traffic to the other fiber cable. In this way,the system 110 may temporarily utilize the other fiber cable for thenetwork traffic, thereby conserving resources that would otherwise havebeen utilized in identifying lost network traffic, attempting to recoverthe lost network traffic, and/or the like.

The one or more actions may include the system 110 providing thelocation of the alarm condition to a requesting system or as part of analerting message or other transmission. The location of the alarmcondition may, in some implementations, include the geographic locationand an additional distance from the geographic location.

In this way, several different stages of the process for identifyinglocations of deployed cables are automated and performed withoutdisruption to physical environment or network operations, which mayremove waste from the process, and improve speed and efficiency of theprocess and conserve computing resources, networking resources, and/orthe like. Furthermore, implementations described herein use a rigorous,computerized process to perform tasks or roles that were not previouslyperformed or were previously performed using subjective human intuitionor input. For example, currently there does not exist a technique thataccurately identifies locations of deployed fiber cables in the mannerdescribed herein. Finally, the process for identifying locations ofdeployed cables conserves computing resources, networking resources,transportation resources, human resources, and/or the like that wouldotherwise have been utilized in identifying an incorrect location of afault in a deployed cable, traveling to the incorrect location,re-identifying a correct location of the fault in the deployed cable,recovering lost network traffic, and/or the like.

As indicated above, FIGS. 1A-1M are provided as an example. Otherexamples may differ from what is described with regard to FIGS. 1A-1M.The number and arrangement of devices shown in FIGS. 1A-1M are providedas an example. In practice, there may be additional devices, fewerdevices, different devices, or differently arranged devices than thoseshown in FIGS. 1A-1M. Furthermore, two or more devices shown in FIGS.1A-1M may be implemented within a single device, or a single deviceshown in FIGS. 1A-1M may be implemented as multiple, distributeddevices. Additionally, or alternatively, a set of devices (e.g., one ormore devices) shown in FIGS. 1A-1M may perform one or more functionsdescribed as being performed by another set of devices shown in FIGS.1A-1M.

FIG. 2 is a diagram illustrating an example 200 of training and using amachine learning model in connection with identifying locations ofdeployed cables. The machine learning model training and usage describedherein may be performed using a machine learning system. The machinelearning system may include or may be included in a computing device, aserver, a cloud computing environment, or the like, such as the system110 described in more detail elsewhere herein.

As shown by reference number 205, a machine learning model may betrained using a set of observations. The set of observations may beobtained from training data (e.g., historical data), such as datagathered during one or more processes described herein. In someimplementations, the machine learning system may receive the set ofobservations (e.g., as input) from the system 110 and/or the sensordevice 115, as described elsewhere herein.

As shown by reference number 210, the set of observations includes afeature set. The feature set may include a set of variables, and avariable may be referred to as a feature. A specific observation mayinclude a set of variable values (or feature values) corresponding tothe set of variables. In some implementations, the machine learningsystem may determine variables for a set of observations and/or variablevalues for a specific observation based on input received from thesystem 110. For example, the machine learning system may identify afeature set (e.g., one or more features and/or feature values) byextracting the feature set from structured data, by performing naturallanguage processing to extract the feature set from unstructured data,and/or by receiving input from an operator.

As an example, a feature set for a set of observations may include afirst feature of vibration pattern, a second feature of vibrationamplitude, a third feature of vibration frequency, and so on. As shown,for a first observation, the first feature may have a value of pattern1, the second feature may have a value of amplitude 1, the third featuremay have a value of frequency 1, and so on. These features and featurevalues are provided as examples, and may differ in other examples.

As shown by reference number 215, the set of observations may beassociated with a target variable. The target variable may represent avariable having a numeric value, may represent a variable having anumeric value that falls within a range of values or has some discretepossible values, may represent a variable that is selectable from one ofmultiple options (e.g., one of multiples classes, classifications, orlabels) and/or may represent a variable having a Boolean value. A targetvariable may be associated with a target variable value, and a targetvariable value may be specific to an observation. In example 200, thetarget variable is environmental characteristic, which has a value oftraffic signal for the first observation. The feature set and targetvariable described above are provided as examples, and other examplesmay differ from what is described above.

The target variable may represent a value that a machine learning modelis being trained to predict, and the feature set may represent thevariables that are input to a trained machine learning model to predicta value for the target variable. The set of observations may includetarget variable values so that the machine learning model can be trainedto recognize patterns in the feature set that lead to a target variablevalue. A machine learning model that is trained to predict a targetvariable value may be referred to as a supervised learning model.

In some implementations, the machine learning model may be trained on aset of observations that do not include a target variable. This may bereferred to as an unsupervised learning model. In this case, the machinelearning model may learn patterns from the set of observations withoutlabeling or supervision, and may provide output that indicates suchpatterns, such as by using clustering and/or association to identifyrelated groups of items within the set of observations.

As shown by reference number 220, the machine learning system may traina machine learning model using the set of observations and using one ormore machine learning algorithms, such as a regression algorithm, adecision tree algorithm, a neural network algorithm, a k-nearestneighbor algorithm, a support vector machine algorithm, or the like.After training, the machine learning system may store the machinelearning model as a trained machine learning model 225 to be used toanalyze new observations.

As shown by reference number 230, the machine learning system may applythe trained machine learning model 225 to a new observation, such as byreceiving a new observation and inputting the new observation to thetrained machine learning model 225. As shown, the new observation mayinclude a first feature of vibration pattern, a second feature ofvibration amplitude, a third feature of vibration frequency, and so on,as an example. The machine learning system may apply the trained machinelearning model 225 to the new observation to generate an output (e.g., aresult). The type of output may depend on the type of machine learningmodel and/or the type of machine learning task being performed. Forexample, the output may include a predicted value of a target variable,such as when supervised learning is employed. Additionally, oralternatively, the output may include information that identifies acluster to which the new observation belongs and/or information thatindicates a degree of similarity between the new observation and one ormore other observations, such as when unsupervised learning is employed.

As an example, the trained machine learning model 225 may predict avalue of cable crosses roadway for the target variable of environmentalcondition for the new observation, as shown by reference number 235.Based on this prediction, the machine learning system may provide afirst recommendation, may provide output for determination of a firstrecommendation, may perform a first automated action, and/or may cause afirst automated action to be performed (e.g., by instructing anotherdevice to perform the automated action), among other examples.

In some implementations, the trained machine learning model 225 mayclassify (e.g., cluster) the new observation in a cluster, as shown byreference number 240. The observations within a cluster may have athreshold degree of similarity. As an example, if the machine learningsystem classifies the new observation in a first cluster (e.g., acrosses roadway cluster), then the machine learning system may provide afirst recommendation. Additionally, or alternatively, the machinelearning system may perform a first automated action and/or may cause afirst automated action to be performed (e.g., by instructing anotherdevice to perform the automated action) based on classifying the newobservation in the first cluster.

As another example, if the machine learning system were to classify thenew observation in a second cluster (e.g., a traffic signal cluster),then the machine learning system may provide a second (e.g., different)recommendation and/or may perform or cause performance of a second(e.g., different) automated action.

In some implementations, the recommendation and/or the automated actionassociated with the new observation may be based on a target variablevalue having a particular label (e.g., classification orcategorization), may be based on whether a target variable valuesatisfies one or more thresholds (e.g., whether the target variablevalue is greater than a threshold, is less than a threshold, is equal toa threshold, falls within a range of threshold values, or the like),and/or may be based on a cluster in which the new observation isclassified.

The recommendations, actions, and clusters described above are providedas examples, and other examples may differ from what is described above.

In this way, the machine learning system may apply a rigorous andautomated process to identifying locations of deployed cables. Themachine learning system enables recognition and/or identification oftens, hundreds, thousands, or millions of features and/or feature valuesfor tens, hundreds, thousands, or millions of observations, therebyincreasing accuracy and consistency and reducing delay associated withidentifying locations of deployed cables relative to requiring computingresources to be allocated for tens, hundreds, or thousands of operatorsto manually identify locations of deployed cables using the features orfeature values.

As indicated above, FIG. 2 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 2 .

FIG. 3 is a diagram of an example environment 300 in which systemsand/or methods described herein may be implemented. As shown in FIG. 3 ,environment 300 may include a system 110, a sensor device 115, and anetwork 310. Devices of environment 300 may interconnect via wiredconnections, wireless connections, or a combination of wired andwireless connections.

The system 110 includes one or more devices capable of receiving,generating, storing, processing, providing, and/or routing informationassociated with identifying locations of deployed cable, as describedelsewhere herein. The system 110 may include a communication deviceand/or a computing device. In some implementations, the system 110 maybe included in the sensor device 115. Alternatively, and/oradditionally, the system 110 may be included in another device. Forexample, the system 110 may be included in a server, such as anapplication server, a client server, a web server, a database server, ahost server, a proxy server, a virtual server (e.g., executing oncomputing hardware), or a server in a cloud computing system. In someimplementations, the system 110 includes computing hardware used in acloud computing environment.

The sensor device 115 may include one or more devices capable ofreceiving, generating, storing, processing, and/or providinginformation, such as information described herein. For example, as notedabove, the sensor device 115 may include optical components, including adistributed optical fiber sensing device—such as a distributed opticalfiber acoustic sensing device that uses a fiber cable to providedistributed strain sensing, where the fiber cable is a sensing elementand vibration measurements are made using an optoelectronic device. Thedistributed optical fiber sensing device may include a Rayleighscattering-based distributed optical fiber acoustic sensing device. Thesensor device 115 may further include an optical reflectometry device,such as an OTDR device. The optical reflectometry device may be usedwith the distributed optical sensing device to perform acousticalsensing of vibrations applied to and experienced by a fiber optic cable.The sensor device 115 may also include or be associated with aprocessing system, such as a laptop computer, a tablet computer, adesktop computer, a handheld computer, or a similar type of device, thatdetermines vibration measurements and fiber cable distances to detectedvibrations. In some implementations, the sensor device 115 may receiveinformation from and/or transmit information to the system 110 through acommunication channel between them, such as the network 310.

The network 310 includes one or more wired and/or wireless networks. Forexample, the network 310 may include a wired network that includes aphysical communication link between the system 110 and the sensor device115. Alternatively, and/or additionally, the network 310 may include awireless wide area network (e.g., a cellular network or a public landmobile network), a local area network (e.g., a wired local area networkor a wireless local area network (WLAN), such as a Wi-Fi network), apersonal area network (e.g., a Bluetooth network), a near-fieldcommunication network, a telephone network, a private network, theInternet, and/or a combination of these or other types of networks. Thenetwork 310 enables communication among the devices of environment 300.

The number and arrangement of devices and networks shown in FIG. 3 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 3 . Furthermore, two or more devices shown in FIG. 3 maybe implemented within a single device, or a single device shown in FIG.3 may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 300 may perform one or more functions described as beingperformed by another set of devices of environment 300.

FIG. 4 is a diagram of example components of a device 400, which maycorrespond to the system 110 and/or the sensor device 115. In someimplementations, the system 110 and/or the sensor device 115 may includeone or more devices 400 and/or one or more components of device 400. Asshown in FIG. 4 , device 400 may include a bus 410, a processor 420, amemory 430, an input component 440, an output component 450, and acommunication component 460.

Bus 410 includes one or more components that enable wired and/orwireless communication among the components of device 400. Bus 410 maycouple together two or more components of FIG. 4 , such as via operativecoupling, communicative coupling, electronic coupling, and/or electriccoupling. Processor 420 includes a central processing unit, a graphicsprocessing unit, a microprocessor, a controller, a microcontroller, adigital signal processor, a field-programmable gate array, anapplication-specific integrated circuit, and/or another type ofprocessing component. Processor 420 is implemented in hardware,firmware, or a combination of hardware and software. In someimplementations, processor 420 includes one or more processors capableof being programmed to perform one or more operations or processesdescribed elsewhere herein.

Memory 430 includes volatile and/or nonvolatile memory. For example,memory 430 may include random access memory (RAM), read only memory(ROM), a hard disk drive, and/or another type of memory (e.g., a flashmemory, a magnetic memory, and/or an optical memory). Memory 430 mayinclude internal memory (e.g., RAM, ROM, or a hard disk drive) and/orremovable memory (e.g., removable via a universal serial busconnection). Memory 430 may be a non-transitory computer-readablemedium. Memory 430 stores information, instructions, and/or software(e.g., one or more software applications) related to the operation ofdevice 400. In some implementations, memory 430 includes one or morememories that are coupled to one or more processors (e.g., processor420), such as via bus 410.

Input component 440 enables device 400 to receive input, such as userinput and/or sensed input. For example, input component 440 may includea touch screen, a keyboard, a keypad, a mouse, a button, a microphone, aswitch, a sensor, a global positioning system sensor, an accelerometer,a gyroscope, and/or an actuator. Output component 450 enables device 400to provide output, such as via a display, a speaker, and/or alight-emitting diode. Communication component 460 enables device 400 tocommunicate with other devices via a wired connection and/or a wirelessconnection. For example, communication component 460 may include areceiver, a transmitter, a transceiver, a modem, a network interfacecard, and/or an antenna.

Device 400 may perform one or more operations or processes describedherein. For example, a non-transitory computer-readable medium (e.g.,memory 430) may store a set of instructions (e.g., one or moreinstructions or code) for execution by processor 420. Processor 420 mayexecute the set of instructions to perform one or more operations orprocesses described herein. In some implementations, execution of theset of instructions, by one or more processors 420, causes the one ormore processors 420 and/or the device 400 to perform one or moreoperations or processes described herein. In some implementations,hardwired circuitry may be used instead of or in combination with theinstructions to perform one or more operations or processes describedherein. Additionally, or alternatively, processor 420 may be configuredto perform one or more operations or processes described herein. Thus,implementations described herein are not limited to any specificcombination of hardware circuitry and software.

The number and arrangement of components shown in FIG. 4 are provided asan example. Device 400 may include additional components, fewercomponents, different components, or differently arranged componentsthan those shown in FIG. 4 . Additionally, or alternatively, a set ofcomponents (e.g., one or more components) of device 400 may perform oneor more functions described as being performed by another set ofcomponents of device 400.

FIG. 5 is a flowchart of an example process 500 associated withidentifying deployed cables. In some implementations, one or moreprocess blocks of FIG. 5 may be performed by a device (e.g., the system110). In some implementations, one or more process blocks of FIG. 5 maybe performed by another device or a group of devices separate from orincluding the device, such as a sensor device (e.g., the sensor device115). Additionally, or alternatively, one or more process blocks of FIG.5 may be performed by one or more components of device 400, such asprocessor 420, memory 430, input component 440, output component 450,and/or communication component 460.

As shown in FIG. 5 , process 500 may include receiving a cable mapassociated with a deployed cable (block 510). For example, the devicemay receive cable map data corresponding to a cable map associated witha deployed cable, as described above.

As further shown in FIG. 5 , process 500 may include receiving vibrationdata indicating a vibration pattern (block 520). For example, the devicemay receive vibration data indicating a vibration pattern associatedwith a first section of the deployed cable, as described above.

As further shown in FIG. 5 , process 500 may include determining acharacteristic of an environment associated with the first section ofthe deployed cable (block 530). For example, the device may determine acharacteristic of an environment associated with the first section ofthe deployed cable based on the vibration pattern, as described above.

As further shown in FIG. 5 , process 500 may include determining alocation associated with the characteristic of the environment (block540). For example, the device may determine a location associated withthe characteristic of the environment based on the cable map data, asdescribed above.

As further shown in FIG. 5 , process 500 may include determining thatthe first section of the deployed cable is associated with the location(block 550). For example, the device may determine that the firstsection of the deployed cable is associated with the location based onthe location being associated with the characteristic of theenvironment, as described above. The characteristic of the environmentmay include a volume of vehicle traffic associated with the environment,a direction of the vehicle traffic, and/or a presence of a trafficsignal within the environment, among other examples.

In some implementations, the device determines the characteristic of theenvironment based on providing the vibration data as an input to amachine learning model to generate an output. The output may indicatethe characteristic of the environment.

As further shown in FIG. 5 , process 500 may include associating thelocation and a length of a second section of the deployed cable (block560). For example, the device may generate cable map data thatassociates the location and a length of a second section of the deployedcable, as described above. In some implementations, the second sectionof the deployed cable extends from an initial location (e.g., a locationof the system 110, a location of the sensor device 115, and/or alocation of a central office, among other examples) to the location. Insome implementations, the second section of the deployed cable isadjacent to the first section of the deployed cable. In someimplementations, the second section of the deployed cable may includethe first section of the deployed cable.

In some implementations, the device may determine a geographicaldistance from the initial location to the location based on the cablemap data. The device may determine a difference between the length ofthe second section of the deployed cable and the geographical distancefrom the initial location to the location. The device may determine anamount of cable included in a slack loop based on the difference betweenthe length of the second section of the deployed cable and thegeographical distance from the initial location to the location. In someimplementations, the device may include information identifying alocation of the slack loop and/or the amount of cable included in theslack loop in the cable map data.

As further shown in FIG. 5 , process 500 may include receiving an inputidentifying the length of the second section of the deployed cable(block 570). For example, the device may receive an input identifyingthe length of the second section of the deployed cable, as describedabove.

As further shown in FIG. 5 , process 500 may include providinginformation identifying the location (block 580). For example, thedevice may provide information identifying the location based onassociating the location and the length of the second section of thedeployed cable, as described above.

In some implementations, the device determines a geographical distancefrom the initial location to the location based on the cable map data.The device may modify the cable map to include information associatingthe location with information identifying the length of the secondsection of the deployed cable and/or a difference between the length ofthe second section of the deployed cable and the geographical distancefrom the initial location to the location.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5 . Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software. Itwill be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, and/or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods are described herein without reference tospecific software code - it being understood that software and hardwarecan be used to implement the systems and/or methods based on thedescription herein.

As used herein, satisfying a threshold may, depending on the context,refer to a value being greater than the threshold, greater than or equalto the threshold, less than the threshold, less than or equal to thethreshold, equal to the threshold, not equal to the threshold, or thelike.

To the extent the aforementioned implementations collect, store, oremploy personal information of individuals, it should be understood thatsuch information shall be used in accordance with all applicable lawsconcerning protection of personal information. Additionally, thecollection, storage, and use of such information can be subject toconsent of the individual to such activity, for example, through wellknown “opt-in” or “opt-out” processes as can be appropriate for thesituation and type of information. Storage and use of personalinformation can be in an appropriately secure manner reflective of thetype of information, for example, through various encryption andanonymization techniques for particularly sensitive information.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set. As used herein, aphrase referring to “at least one of” a list of items refers to anycombination of those items, including single members. As an example, “atleast one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c,and a-b-c, as well as any combination with multiple of the same item.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterm “set” is intended to include one or more items (e.g., relateditems, unrelated items, or a combination of related and unrelateditems), and may be used interchangeably with “one or more.” Where onlyone item is intended, the phrase “only one” or similar language is used.Also, as used herein, the terms “has,” “have,” “having,” or the like areintended to be open-ended terms. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise. Also, as used herein, the term “or” is intended to beinclusive when used in a series and may be used interchangeably with“and/or,” unless explicitly stated otherwise (e.g., if used incombination with “either” or “only one of”).

In the preceding specification, various example embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe broader scope of the invention as set forth in the claims thatfollow. The specification and drawings are accordingly to be regarded inan illustrative rather than restrictive sense.

What is claimed is:
 1. A method, comprising: receiving, by the device,vibration data indicating a vibration pattern associated with a firstsection of a deployed cable; determining, by the device, acharacteristic of an environment associated with the first section ofthe deployed cable based on the vibration pattern; determining, by thedevice, a location associated with the characteristic of the environmentbased on cable map data associated with the deployed cable; determining,by the device, that the first section of the deployed cable isassociated with the location based on the location being associated withthe characteristic of the environment; associating, by the device, thelocation associated with the characteristic of the environment and alength of a second section of the deployed cable, wherein the secondsection of the deployed cable extends from an initial location to thelocation associated with the characteristic of the environment;receiving, by the device, an input identifying the length of the secondsection of the deployed cable; and providing, by the device, informationidentifying the location associated with the characteristic of theenvironment based on associating the location associated with thecharacteristic of the environment and the length of the second sectionof the deployed cable.
 2. The method of claim 1, further comprising:determining a geographical distance from the initial location to thelocation associated with the characteristic of the environment based onthe cable map data; and modifying the cable map to include informationassociating the location associated with the characteristic of theenvironment with information identifying one or more of: the length ofthe second section of the deployed cable, or a difference between thelength of the second section of the deployed cable and the geographicaldistance from the initial location to the location associated with thecharacteristic of the environment.
 3. The method of claim 1, whereindetermining the characteristic of the environment comprises: providingthe vibration data as an input to a machine learning model to generatean output; and determining the characteristic of the environment basedon the output.
 4. The method of claim 1, wherein the characteristic ofthe environment includes one or more of: a volume of vehicle trafficassociated with the environment, a direction of the vehicle traffic, ora presence of a traffic signal within the environment.
 5. The method ofclaim 1, wherein the first section of the deployed cable is adjacent tothe second section of the deployed cable.
 6. The method of claim 1,wherein the second section of the deployed cable includes the firstsection of the deployed cable.
 7. The method of claim 1, furthercomprising: determining a geographical distance from the initiallocation to the location associated with the characteristic of theenvironment based on the cable map data; determining a differencebetween the length of the second section of the deployed cable and thegeographical distance from the initial location to the locationassociated with the characteristic of the environment; and determiningan amount of cable included in a slack loop based on the differencebetween the length of the second section of the deployed cable and thegeographical distance from the initial location to the locationassociated with the characteristic of the environment.
 8. Anon-transitory computer-readable medium storing a set of instructions,the set of instructions comprising: one or more instructions that, whenexecuted by one or more processors of a device, cause the device to:receive vibration data indicating a vibration pattern associated with afirst section of a deployed cable; determine a characteristic of anenvironment associated with the first section of the deployed cablebased on the vibration pattern; identify a location associated with thecharacteristic of the environment based on map data associated with thedeployed cable; determine that the location associated with thecharacteristic of the environment is associated with the first sectionof the deployed cable based on the location being associated with thecharacteristic of the environment; associate the location associatedwith the characteristic of the environment and a length of a secondsection of the deployed cable, wherein the second section of thedeployed cable extends from an initial location to the locationassociated with the characteristic of the environment; receive an inputidentifying the length of the second section of the deployed cable; andprovide information identifying the location associated with thecharacteristic of the environment based on associating the location andthe length of the second section of the deployed cable.
 9. Thenon-transitory computer-readable medium of claim 8, wherein the one ormore instructions further cause the device to: determine a distance fromthe initial location to the location associated with the characteristicof the environment based on the map data; and modify the map data toinclude information associating the location associated with thecharacteristic of the environment with information identifying one ormore of: the length of the second section of the deployed cable, or adifference between the length of the second section of the deployedcable and the distance from the initial location to the locationassociated with the characteristic of the environment.
 10. Thenon-transitory computer-readable medium of claim 8, wherein the one ormore instructions, that cause the device to determine the characteristicof the environment, cause the device to: provide the vibration data asan input to a machine learning model to generate an output; anddetermine the characteristic of the environment based on the output. 11.The non-transitory computer-readable medium of claim 8, wherein thecharacteristic of the environment includes one or more of: a volume ofvehicle traffic associated with the environment, a direction of thevehicle traffic, or a presence of a traffic signal within theenvironment.
 12. The non-transitory computer-readable medium of claim 8,wherein the characteristic of the environment includes one or more of:information indicating that the location associated with thecharacteristic of the environment is associated with a particular typeof cable, or information indicating whether the second section of thedeployed cable is installed above a surface of the environment.
 13. Thenon-transitory computer-readable medium of claim 8, wherein the one ormore instructions further cause the device to: determine a distance fromthe initial location to the location associated with the characteristicof the environment based on the map data associated with the deployedcable; determine a difference between the length of the second sectionof the deployed cable and the distance from the initial location to thelocation associated with the characteristic of the environment; anddetermine an amount of cable included in a slack loop based on thedifference between the length of the second section of the deployedcable and the distance from the initial location to the locationassociated with the characteristic of the environment.
 14. Thenon-transitory computer-readable medium of claim 13, wherein the one ormore instructions further cause the device to: modify the map data toassociate the location with information associated with the slack loop.15. A device, comprising: one or more processors configured to:determine cable map data associated with a deployed cable, wherein thecable map data identifies a location associated with the deployed cable;receive vibration data indicating a vibration pattern associated with afirst section of the deployed cable; determine a characteristic of anenvironment associated with the first section of the deployed cablebased on the vibration pattern; determine that the location isassociated with the first section of the deployed cable based on thecharacteristic of the environment; associate the location and a lengthof a second section of the deployed cable, wherein the second section ofthe deployed cable extends from an initial location to the location;receive an input identifying the length of the second section of thedeployed cable; and provide information identifying the location basedon associating the location and the length of the second section of thedeployed cable.
 16. The device of claim 15, wherein the one or moreprocessors are further configured to: determine a distance from theinitial location to the location based on the cable map data; and modifythe cable map data to include information associating the location withinformation identifying one or more of: the length of the second sectionof the deployed cable, or a difference between the length of the secondsection of the deployed cable and the distance from the initial locationto the location.
 17. The device of claim 15, wherein the one or moreprocessors, to determine the characteristic of the environment, areconfigured to: provide the vibration data as an input to a machinelearning model to generate an output; and determine the characteristicof the environment based on the output.
 18. The device of claim 15,wherein the characteristic of the environment includes one or more of: avolume of vehicle traffic associated with the environment, a directionof the vehicle traffic, or a presence of a traffic signal within theenvironment.
 19. The device of claim 15, wherein the characteristic ofthe environment includes one or more of: information indicating that thelocation is associated with a particular type of cable, or informationindicating whether the second section of the deployed cable is installedabove a surface of the environment.
 20. The device of claim 15, whereinthe one or more processors are further configured to: determine adifference between the length of the second section of the deployedcable and a distance from the initial location to the location; anddetermine an amount of cable included in a slack loop based on thedifference between the length of the second section of the deployedcable and the distance from the initial location to the location.